Joint Optimization of Condition-Based Maintenance and Spare Parts Ordering for a Hidden Multi-State Deteriorating System

In the past decade, the sensor and surveillance technology have been widely used in condition monitoring. The hidden states of systems can be inferred from collected sensor data. However, the maintenance decision problem becomes more challenging when ordering decision of the spare parts must be considered jointly. In this article, we consider a multistate deteriorating system whose states are hidden but partially observable, and determine the optimal maintenance and spare parts inventory ordering policy. We use the partially observable Markov decision process to model the problem of interest and adopt the state-of-the-art heuristic search value iteration algorithm to solve the optimization problem. The proposed policy is illustrated and compared with$( {{\bm{s}},{\bm{S}}} )$inventory policy through a series of numerical examples. The numerical results indicate that our proposed policy is cost effective. Further, the model of multicomponent system is formulated, highlighting the adaptability of our framework. This research shows that considering the system component conditions and spare parts ordering jointly can result a lower operation and maintenance cost.